What you’ll learn:
Master the use of the R and R Studio interactive environment
How to download R programming language and R Studio IDE
Getting familiar with the Console, Script, Environment, Help, and Plots panels.
Understanding different data types in R
How to create, manipulate and work with different data types like Vectors, Matrices, Lists, Arrays, and Data frames.
Installing and loading the Packages in the R environment.
Reading and writing various Data Files like CSV, Xls, XML, JSON, xlsx, txt.
Using various functions associated with the data frames and performing data manipulation on the data frames/files.
Performing data Transformation and data analysis on the data files and data frames.
Getting familiar with the packages: ggplot2, tidyverse, dplyr, plotly
How to load and work upon various datasets which are native to R.
Performing data visualization by creating various plots.
learn to draw plots in R studio: Pie Charts, Bar Charts, Box plots, Histograms, Line Graphs, Scatter Plots.
Learn how to draw some advanced plots like Density charts, Bubble plots, lollipop charts, 3D charts in R studio
How to perform Data Analysis and Visualization using R in R studio.
Install External Libraries to Power up R
Compute Basic Statistics about a Dataset
Develop your own Functions in R
Requirements:
No prior knowledge of R or R Studio is required. You will learn all from the basics.
Description:
This course will teach you R programming and how to program R in R Studio along with its usage for effective data analysis. You will be able to master the basics of R, including the lists, vectors, and data frames.
The course provides practical knowledge about programming in R, reading and writing data files into R, loading and installing the R packages, loading and working upon various datasets, data transformation techniques, creating and handling various data types, data analysis, and visualization by creating various kinds of plots. R is very actively used for statistical computing and designing. It is one of the most widely used languages in the data science sector.
